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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>An optimization framework for risk response actions selection using hybrid ACO and FTOPSIS</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1763</FirstPage>
			<LastPage>1777</LastPage>
			<ELocationID EIdType="pii">20225</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20225</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Sh.</FirstName>
					<LastName>Shoar</LastName>
<Affiliation>Department of Project &amp; Construction Management, Mehralborz Institute of Higher Education, No. 109, Shokrollah Street, Jajal-e Al-e Ahmad Crossroad, North Kargar Avenue, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>A.</FirstName>
					<LastName>Nazari</LastName>
<Affiliation>Department of Construction, Faculty of Architecture and Urban Design, Shahid Beheshti University, Tehran, Iran.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2016</Year>
					<Month>07</Month>
					<Day>27</Day>
				</PubDate>
			</History>
		<Abstract>This paper presents a framework for solving risk response action selection problem by considering: (1) the impact of risk events on the project objectives, (2) the interactions between risk events and (3) management criteria and preferences. For these purposes, a framework is developed by combining an optimization-based model and a Multi Criteria Decision Making (&lt;em&gt;MCDM&lt;/em&gt;) approach. First, in the optimization-based model, Ant Colony Optimization (&lt;em&gt;ACO&lt;/em&gt;) is used to find the best combination of response actions which have more effects on time, cost and quality. Also, in this model, to overcome the imprecision situation resulting from lack of knowledge or insufficient data, risk parameters are determined using the fuzzy set theory.  Moreover, the Design Structure Matrix&lt;em&gt; (DSM) &lt;/em&gt;is used to capture the effect of interactions between risk events. Second, theFuzzy Technique for Order Preference by Similarity to Ideal Solution &lt;em&gt;(FTOPSIS) &lt;/em&gt;method is used to analyze the obtained solutions by &lt;em&gt;ACO, &lt;/em&gt;based on the other management criteria. Finally, the efficiency of the proposed framework is examined by its implementation in a real building construction project. Discussions through the case study show that using the proposed framework decision makers can evaluate more aspects of response actions.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Project risk management</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Risk response action selection</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">ACO</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Risk interactions</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">FTOPSIS</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Fuzzy set theory</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20225_99bbb17674e75c528140126a3deb730e.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>A class Hotelling model for sequential auctions of close substitutes</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1778</FirstPage>
			<LastPage>1788</LastPage>
			<ELocationID EIdType="pii">20546</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20546</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>E.</FirstName>
					<LastName>Hu</LastName>
<Affiliation>Systems Engineering Institute of Automation School, Huazhong University of Science and  Technology, Wuhan 430074, P. R. China.;School of Science, Hubei University of Technology, Wuhan 430068, P. R. China.</Affiliation>

</Author>
<Author>
					<FirstName>C.</FirstName>
					<LastName>Rao</LastName>
<Affiliation>School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China.</Affiliation>
<Identifier Source="ORCID">0000-0002-1045-559X</Identifier>

</Author>
<Author>
					<FirstName>Y.</FirstName>
					<LastName>Zhao</LastName>
<Affiliation>Systems Engineering Institute of Automation School, Huazhong University of Science and Technology, Wuhan 430074, P. R.
China.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2016</Year>
					<Month>09</Month>
					<Day>10</Day>
				</PubDate>
			</History>
		<Abstract>Against the background of supply chains, this paper constructs a class Hotelling model to describe and explore sequential auctions of close substitutes with slightly more general associated valuations. In this generalized model, both close substitutes and bidders are hypothetically distributed in the interval [0, 1], types of bidders are continuous, and each bidder’s valuations for close substitutes are not independent. And with the aid of this model, equilibriums are explored and efficiencies of the auctions are analyzed under second-price sealed-bid auction formats. Further considering two typical information policies, we investigate some concrete bids and revenues of the efficient sequential auctions while bidders’ valuations are linear functions of distances between them and close substitutes. Results show that efficiencies of the sequential auctions are conditional, and influences of information policies on revenues of the auctions are related to both numbers of bidders and locations of items.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Sequential Auctions</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Hotelling Model</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Supply chain</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Associated Valuation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Information Policy</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20546_b8c55b4de0a7321787335bfe85ce8256.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Bayesian analysis of heterogeneous doubly censored lifetime data using the 3-component mixture of Rayleigh distributions: A Monte Carlo simulation study</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1789</FirstPage>
			<LastPage>1808</LastPage>
			<ELocationID EIdType="pii">20606</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20606</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Tahir</LastName>
<Affiliation>Department of Statistics, Government College University, Faisalabad 38000, Pakistan</Affiliation>

</Author>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Aslam</LastName>
<Affiliation>Department of Mathematics and Statistics, Riphah International University, Islamabad 44000, Pakistan.</Affiliation>
<Identifier Source="ORCID">0000-0003-0644-1950</Identifier>

</Author>
<Author>
					<FirstName>Z.</FirstName>
					<LastName>Hussain</LastName>
<Affiliation>Department of Statistics, Quaid-i-Azam University, Islamabad 44000, Pakistan.</Affiliation>
<Identifier Source="ORCID">0000-0002-5109-3652</Identifier>

</Author>
<Author>
					<FirstName>Muhammad</FirstName>
					<LastName>A.</LastName>
<Affiliation>Department of Statistics, Government College University, Faisalabad 38000, Pakistan</Affiliation>

</Author>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Haider Bhatti</LastName>
<Affiliation>Department of Statistics, Government College University, Faisalabad 38000, Pakistan.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2016</Year>
					<Month>11</Month>
					<Day>02</Day>
				</PubDate>
			</History>
		<Abstract>This article is about Bayesian estimation of parameters of a heterogeneous 3-component mixture of Rayleigh distributions (3-CMRD) generating a mixture data. Being the most popular and reasonable sampling scheme in reliability and survival analyses, the doubly censored sampling scheme is considered. The Bayes estimators and their posterior risks are derived under various situations. In addition, elicitation of hyperparameters is presented. Algebraic expressions for posterior predictive distribution and Bayesian predictive intervals are derived.  Assuming the informative and the non-informative priors, a comprehensive Monte Carlo simulation is conducted to examine the performance of the Bayes estimators under symmetric and asymmetric loss functions. Finally, to highlight the practical importance, the proposed 3-compnent mixture model is applied to a doubly censored lifetime data from a real life situation. It is observed that the analysis of doubly censored data in Bayesian framework, the SRIGP paired with SELF (DLF) is suitable choice for estimating mixing proportion (component) parameters.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Mixture model</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Informative priors</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Doubly censored sampling scheme</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Non-informative priors</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Bayesian predictive interval</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Posterior risk</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20606_0d10f1d273a62c6d7af57c6093632919.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Hybrid fuzzy-stochastic approach to multi-product, multi-period, and multi-resource master production scheduling problem: Case of a polyethylene pipe and Fitting manufacturer</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1809</FirstPage>
			<LastPage>1823</LastPage>
			<ELocationID EIdType="pii">20329</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20329</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>S.H.</FirstName>
					<LastName>Razavi Hajiagha</LastName>
<Affiliation>Department of  Management, Khatam University, Tehran, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>Sh.</FirstName>
					<LastName>Hashemi</LastName>
<Affiliation>Department of Management, Saramadan Andishe Avina Co. Tehran, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Sadeghi</LastName>
<Affiliation>Industrial Management Group, Faculty of Management and Accounting, Allameh Tabatabaei University, Tehran, Iran.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2016</Year>
					<Month>11</Month>
					<Day>22</Day>
				</PubDate>
			</History>
		<Abstract>Master production scheduling is an effective phase of production planning which leads to scheduling and magnitude of different products production in a company. This problem requires investigating a wide range of parameters, regarding demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty, in which demands, as time-dependent variables, are considered as stochastic variables, while cost and utilization parameters, with cognitive ambiguity, are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a practical problem of a polyethylene pipe and fitting Co. in Iran. The result showed a high degree of applicability.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Master production scheduling</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Stochastic demand</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Chance constrained programming</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Fuzzy set theory</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20329_d72b69c8e37aec662e13e39d929d6e3d.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>The surgical case scheduling problem with fuzzy duration time: An ant system algorithm</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1824</FirstPage>
			<LastPage>1841</LastPage>
			<ELocationID EIdType="pii">20602</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20602</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>R.</FirstName>
					<LastName>Behmanesh</LastName>
<Affiliation>Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.</Affiliation>
<Identifier Source="ORCID">0000-0002-0990-1994</Identifier>

</Author>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Zandieh</LastName>
<Affiliation>Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, G.C., Tehran, Iran.</Affiliation>
<Identifier Source="ORCID">0000-0003-1209-9514</Identifier>

</Author>
<Author>
					<FirstName>S.M.</FirstName>
					<LastName>Hadji Molana</LastName>
<Affiliation>Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>03</Month>
					<Day>31</Day>
				</PubDate>
			</History>
		<Abstract>In this paper, we address the surgical case scheduling problem in multi operating theater environment with uncertain service times in order to minimize makespan. In surgical case scheduling, not only the hospital resources are allocated to surgical cases but also the start time of performing surgeries is determined based on sequence of cases in a short-term time horizon. We consider fuzzy numbers for duration times of all stages and hereafter the problem called fuzzy surgical case scheduling. Since the operational environment in the problem is similar to no-wait multi-resource fuzzy flexible job shop problem, we consider constraints of that for formulating and solving problem. This problem is strongly an NP-hard optimization problem, hence we employ ant system algorithm to tackle problem. The proposed approach is illustrated by detailed examples of three test cases, and numerical computational experiments. Therefore, the performance of proposed algorithm is compared with a schedule constructed by first-come-first-service rule on all test instances. Also, a real case is provided from Isfahan’s hospital to evaluate proposed algorithm.  Consequently, computational experiments state that algorithm outperforms results obtained by hospital planning as well as fuzzy rule, and these indicate efficiency and capability of our algorithm for optimizing the makespan.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Surgical case scheduling</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Ant System</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Operating theater</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Fuzzy duration time</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Makespan</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Mixed integer programming</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20602_286b0b3ea509af1aeff6bb47299d96d7.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>A stochastic multi-objective model based on the classical optimal search model for searching for the people who are lost in response stage of earthquake</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1842</FirstPage>
			<LastPage>1864</LastPage>
			<ELocationID EIdType="pii">20226</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20226</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>A.</FirstName>
					<LastName>Sotoudeh-Anvari</LastName>
<Affiliation>Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>S.J.</FirstName>
					<LastName>Sadjadi</LastName>
<Affiliation>Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran</Affiliation>
<Identifier Source="ORCID">0000-0002-5151-8315</Identifier>

</Author>
<Author>
					<FirstName>S.M.</FirstName>
					<LastName>Hadji Molana</LastName>
<Affiliation>Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.</Affiliation>

</Author>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Sadi-Nezhad</LastName>
<Affiliation>Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>03</Month>
					<Day>11</Day>
				</PubDate>
			</History>
		<Abstract>Although after an earthquake the injured person should be equipped with food, shelter and hygiene activities, before anything must be searched and rescued. But disaster management (DM) has focused heavily on emergency logistics and developing an effective strategy for search operations has been largely ignored. In this study, we suggest a stochastic multi-objective optimization model to allocate resource and time for searching the individuals who are trapped in disaster regions. Since in disaster conditions the majority of information is uncertain, our model assumes ambiguity for the locations where the missing people may exist. Fortunately, the suggested model fits nicely into the structure of the classical optimal search model. Hence, we use a stochastic dynamic programming approach to solve this problem. On the other hand, through a computational experiment, we have observed that this model needs heavy computation. Therefore, we reformulate the suggested search model as a multi-criteria decision making (MCDM) problem and employ two efficient MCDM techniques, i.e. TOPSIS and COPRAS to tackle this ranking problem. Consequently, the computational effort is decreased significantly and a promising solution is produced.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Earthquake response</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">multi-objective optimization</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Search theory</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Dynamic programming</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Multi-criteria decision making</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20226_aaa97bcc0fca801927941e0ab185442f.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>A queuing theory-based approach to designing cellular manufacturing systems</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1865</FirstPage>
			<LastPage>1880</LastPage>
			<ELocationID EIdType="pii">20425</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.5020.1047</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>K.</FirstName>
					<LastName>Forghani</LastName>
<Affiliation>Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran</Affiliation>
<Identifier Source="ORCID">0000-0003-3040-261X</Identifier>

</Author>
<Author>
					<FirstName>S.M.T.</FirstName>
					<LastName>Fatemi Ghomi</LastName>
<Affiliation>Department of Industrial Engineering, Amirkabir University of Technology,424 Hafez Avenue,Tehran,Iran</Affiliation>
<Identifier Source="ORCID">0000-0003-4363-994X</Identifier>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>08</Month>
					<Day>15</Day>
				</PubDate>
			</History>
		<Abstract>This paper presents a new cell formation and cell layout problem considering multiple process routings and subcontracting using the principles of queuing theory. It is assumed that each machine operates as an M/M/1 queuing system and a queuing network is used to obtain in-process inventories and machine utilization. The problem is formulated as a mixed-integer nonlinear program with the objective of minimizing the total costs, including the production, subcontracting, material handling, machine idleness, and holding costs. Due to the computational complexity of the problem, a heuristic method is suggested to effectively solve the problem. A numerical example is given to clarify the proposed approach, and finally, further instances are solved to verify the performance of the solution method and to accomplish comparisons. The computational results show that the proposed heuristic is both effective and efficient.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">cell formation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">facility layout</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">queueing network</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">routing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">outsourcing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">heuristic method</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20425_6e40bb76aed065932b7314cf7ea9629b.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Robust and sustainable full-shipload routing and scheduling problem considering variable speed: A real case study</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1881</FirstPage>
			<LastPage>1897</LastPage>
			<ELocationID EIdType="pii">20561</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.5106.1100</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Rabbani</LastName>
<Affiliation>School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Sadeghsa</LastName>
<Affiliation>School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Vaez-Alaei</LastName>
<Affiliation>School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>H.</FirstName>
					<LastName>Farrokhi-Asl</LastName>
<Affiliation>School of Industrial Engineering, Iran University of Science &amp;amp; Technology, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>09</Month>
					<Day>01</Day>
				</PubDate>
			</History>
		<Abstract>This paper presents a sustainable multi objective routing and scheduling for maritime transportation considering ship variable speeds under uncertainty. The proposed model is aimed to satisfy three individual dimensions of sustainability including economic, environmental and social aspects simultaneously while it is finding the best routes and schedule for each ship. The first objective is placed to meet economic goal by minimizing shipping cost. The second objective goes to social respect of sustainability by maximizing job creation due to number of intransitive workers in ships and ports and, the third one is minimizing CO2 emission to cover environmental target. Several test problems are applied to validate the proposed model and sensitivity analysis is used to demonstrate effects of model’s parameters on objective function value. Augmented ɛ-constraint is implemented as a solution method to solve the multi-objective mathematical model. This is the first ship routing and scheduling paper which is considered three aspect of sustainability under uncertainty and solved by augmented ɛ-constraint. To solve the model in larger size, factual input data from a real case study is considered. Computational results show a significant positive managerial effects of this paper contributions.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Maritime transportation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Speed optimization</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Carbon emission</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Sustainability</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">fuzzy</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">robust optimization</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20561_65d1b8a382fe0421b1c1d5b932baf87a.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>The algebraic structures of complex intuitionistic fuzzy soft sets associated with groups and subgroups</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1898</FirstPage>
			<LastPage>1912</LastPage>
			<ELocationID EIdType="pii">20438</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.50050.1485</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>S.G.</FirstName>
					<LastName>Quek</LastName>
<Affiliation>aA-Level Academy, UCSI College KL Campus, Lot 12734, Jalan Choo Lip Kung Taman Taynton View, 56000 Cheras,Kuala Lumpur, MALAYSIA</Affiliation>

</Author>
<Author>
					<FirstName>G.</FirstName>
					<LastName>Selvachandran</LastName>
<Affiliation>Department of Actuarial Science and Applied Statistics, Faculty of Business and Information Science,
UCSI University, Jalan Menara Gading, 56000 Cheras, Kuala Lumpur, MALAYSIA</Affiliation>

</Author>
<Author>
					<FirstName>B.</FirstName>
					<LastName>Davvaz</LastName>
<Affiliation>Department of Mathematics, Yazd University, Yazd, Iran</Affiliation>

</Author>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Pal</LastName>
<Affiliation>Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, West Bengal, India.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>12</Month>
					<Day>19</Day>
				</PubDate>
			</History>
		<Abstract>In recent years, the theory of complex fuzzy sets has captured the attention of many researchers, and research in this area has intensified in the past five years. However, almost all of the researchers in this area have focused on the development of various complex fuzzy based models as well as constructing decision making processes using current decision making approaches and tools. In this spirit, this paper focuses on developing the algebraic structures pertaining to groups and subgroups for the complex intuitionistic fuzzy soft set model. This paper was constructed based on the complex intuitionistic fuzzy soft set model which is characterized by a membership and a non-membership structure for both the amplitude and phase terms of the elements. This model was chosen due to its dual-membership structure that is better able to handle the uncertainties and partial ignorance that exists in most complex data, whilst retaining all the characteristics and advantages of complex fuzzy sets. Besides examining the properties and structural characteristics of the algebraic structures, the relationship between the algebraic structures introduced here and the corresponding algebraic structures in fuzzy group theory and classical group theory were also discussed and verified.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Fuzzy group</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Intuitionistic fuzzy group</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Complex fuzzy set</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">complex intuitionistic fuzzy soft set</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">intuitionistic fuzzy soft group</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">fuzzy soft group</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20438_d89af72662f49ece4d09dec75a8b0166.pdf</ArchiveCopySource>
</Article>

<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>26</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Modeling stock-out loss and overstocking loss generated by bullwhip effect</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1913</FirstPage>
			<LastPage>1924</LastPage>
			<ELocationID EIdType="pii">20199</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2018.20199</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Zanddizari</LastName>
<Affiliation>Robert H. Smith School of Business, University of Maryland, College Park, MD, USA</Affiliation>

</Author>
<Author>
					<FirstName>R.</FirstName>
					<LastName>Tavakkoli-Moghaddam</LastName>
<Affiliation>Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>A.</FirstName>
					<LastName>Azaron</LastName>
<Affiliation>Department of Industrial Engineering, Istanbul Sehir University, Istanbul, Turkey.;
 Beedie School of Business, Simon Fraser University, Vancouver, Canada.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>07</Month>
					<Day>21</Day>
				</PubDate>
			</History>
		<Abstract>Although the literature of the supply chain is teemed with the analysis of the bullwhip effect, few studies regarding the impact of the bullwhip effect or demand distortion on the supply chain profit have been done. Hence, we introduce the concept of Distance to Loss (DL), which is a function of the retailer’s selling price, the manufacturer’s wholesaler price, the end item’s salvage value, the retailer’s expected demand and the retailer’s variance of demand. This concept can perfectly model both stock-out loss and overstocking loss emanated by the bullwhip effect and combines both the newsvendor model and credit risk concepts. Our findings are based on an experimental design and are profoundly in line with previous research. In particular, our model indicates that variations in demand parameters, retailer’s selling price and manufacturer’s wholesaler price impinge on the retailer’s DL, whereas a slight increase in the salvage value negligibly affect the retailer’s DL.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Supply chain management</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Inventory control</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Bullwhip effect</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">KMV model</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Distance to default</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_20199_78bfc4fdafe38bbdb63f9afa4813e26b.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
